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Regular version of the site
Master 2020/2021

## Econometrics (Advanced Level)

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Compulsory course (Financial Strategies and Analytics)
Area of studies: Finance and Credit
When: 1 year, 1-3 module
Mode of studies: offline
Master’s programme: Financial Strategies and Analitics
Language: English
ECTS credits: 8

### Course Syllabus

#### Abstract

The course «Advanced econometrics» is designed for first-year graduate master students following the program «Finance». Its main goal is to familiarize the students with advanced methods of econometric research, and their application to finance area using the appropriate software. The important accent is made on the selection of adequate econometric methods and program tools for the solution of research problems which could arise during the analysis of financial markets.

#### Learning Objectives

• Providing a theoretical knowledge about state-of-the-art econometrical methods of data analysis.
• Forming practical skills of application of econometrical methods.
• Developing of skills of work with specialized statistical software.

#### Expected Learning Outcomes

• Students knows the theoretical base of econometrics and basic methods of analysis.
• Students knows and uses advanced econometric methods.

#### Course Contents

• Classical linear regression model
1. Simple linear regression. Introduction. OLS and its key assumptions. Estimation of simple linear regression by OLS. The quality of model fitting. 2. Multiple linear regression. Multiple linear regression in scalar and matrix forms. The main properties of the model: unbiasedness, consistency and efficiency. Hypothesis checking, testing of coefficients and of linear restrictions. 3. Violation of assumptions in linear regression models. Multicollinearity. Heteroskedasticity. Autocorrelation. Incorrect specification in respect to variables, errors and model form. The White, Breusch-Pagan and Durbin-Watson tests. 4. Nonlinear models. Binary choice models. Multiple response models. Censored models. Selection bias.
• Time series analysis
5. Stationary Time Series. Dealing with time-series data. Concept of stationary and covariate-stationary series, autocorrelation and partial-autocorrelation functions, white Noise, autoregression models (AR), moving average (MA) models, ARMA models: properties, specification, estimation. Statistical tests for stationary series. 6. Nonlinearities in mean. Models with structural breaks. Testing for the structural break. Threshold Autoregressive (TAR) Model: properties, specification, estimation. SETAR model as extension of TAR model: properties, specification, estimation. Smooth transition model: properties, specification, estimation. Posttesting. 7. Nonlinearities in variance. Conditional heteroscedastic models. Testing for ARCH effect. ARCH model: properties, specification, estimation. GARCH model: properties, specification, estimation. Posttesting. 8. State space models, introduction. Local state space model, local trend state space model: properties, specification, statistical inference. Kalman filter. State smoothing. 9. Basic principles of forecasting. Point forecast. The confidence interval for point forecast. Forecasting schemes: fixed window, expanding window and rolling window. Forecast combination. Accuracy metrics.
• Microeconometrics models
5. Instrumental variables estimation. Endogeneity: causes and consequences. Methods of treating: IV, 2SLS, GMM. Instrumental variables: validity, relevance and their testing. 6. Panel data models. Panel structure of data. Fixed and random effects. Endogeneity in panel data. Hausman-Taylor model. Dynamic models. Arellano-Bond model. Mixed models.

#### Assessment Elements

• Test 1
• Seminar activities 1
• Independent work 1
• Exam 1
• Seminar activities 2
After each seminar session (in the second module) students prepare a report of performed tasks in a written form. Each written report is assessed on a 10-point scale with 10 points for correctly and accurately performed tasks. If not, the grade will be smaller proportional to the number of mistakes or omitted tasks. The overall grade for seminar activities calculated as an average grade of all reports.
• Self-study work 2 (DataCamp)
• Exam 2
The exam is a test for 60 minutes on Smart LMS platform (edu.hse.ru). The exam covers topics only on Time Series Analysis (part 2 of the course)
• Test 3
• Independent work 3
• Exam 3
Экзамен в 3 модуле проводится очно в письменной форме
• Seminar activities 3

#### Interim Assessment

• Interim assessment (1 module)
0.4 * Exam 1 + 0.1 * Independent work 1 + 0.25 * Seminar activities 1 + 0.25 * Test 1
• Interim assessment (2 module)
0.4 * Exam 2 + 0.1 * Self-study work 2 (DataCamp) + 0.5 * Seminar activities 2
• Interim assessment (3 module)
0.132 * Exam 3 + 0.033 * Independent work 3 + 0.33 * Interim assessment (1 module) + 0.34 * Interim assessment (2 module) + 0.066 * Seminar activities 3 + 0.099 * Test 3

#### Recommended Core Bibliography

• Introductory econometrics : a modern approach [Lecture notes on econometrics 2], Wooldridge J.M., 2012
• Tsay, R. S. (2010). Analysis of Financial Time Series (Vol. 3rd ed). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=334288
• Tsay, R. S. (2013). An Introduction to Analysis of Financial Data with R. Wiley.

#### Recommended Additional Bibliography

• Microeconometrics using stata, Cameron A.C., Trivedi P.K., 2010